


"""simple docstring"""



import os
import sys
import unittest


lowercase_							=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase_							=							os.path.join(git_repo_path, 'src', 'diffusers')







class    __a						( unittest.TestCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			find_backend('''    if not is_torch_available():'''   )
					self.assertEqual(UpperCAmelCase      ,	'''torch'''   )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					lowerCAmelCase_      							=			find_backend('''    if not (is_torch_available() and is_transformers_available()):'''   )
					self.assertEqual(UpperCAmelCase      ,	'''torch_and_transformers'''   )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					lowerCAmelCase_      							=			find_backend(
					    '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''   )
					self.assertEqual(UpperCAmelCase      ,	'''torch_and_transformers_and_onnx'''   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn('''torch'''      ,	UpperCAmelCase   )
					self.assertIn('''torch_and_transformers'''      ,	UpperCAmelCase   )
					self.assertIn('''flax_and_transformers'''      ,	UpperCAmelCase   )
					self.assertIn('''torch_and_transformers_and_onnx'''      ,	UpperCAmelCase   )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn('''UNet2DModel'''      ,	objects['''torch''']   )
					self.assertIn('''FlaxUNet2DConditionModel'''      ,	objects['''flax''']   )
					self.assertIn('''StableDiffusionPipeline'''      ,	objects['''torch_and_transformers''']   )
					self.assertIn('''FlaxStableDiffusionPipeline'''      ,	objects['''flax_and_transformers''']   )
					self.assertIn('''LMSDiscreteScheduler'''      ,	objects['''torch_and_scipy''']   )
					self.assertIn('''OnnxStableDiffusionPipeline'''      ,	objects['''torch_and_transformers_and_onnx''']   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			create_dummy_object('''CONSTANT'''      ,	'''\'torch\''''   )
					self.assertEqual(UpperCAmelCase      ,	'''\nCONSTANT = None\n'''   )

					lowerCAmelCase_      							=			create_dummy_object('''function'''      ,	'''\'torch\''''   )
					self.assertEqual(
					    UpperCAmelCase      ,	'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''   )

					lowerCAmelCase_      							=			'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
					lowerCAmelCase_      							=			create_dummy_object('''FakeClass'''      ,	'''\'torch\''''   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
					lowerCAmelCase_      							=			create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}   )
					self.assertEqual(dummy_files['''torch''']      ,	UpperCAmelCase   )



"""simple docstring"""



from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class    __a						( __snake_case							):







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = None      ,	UpperCAmelCase = True      ,	UpperCAmelCase = None      ,	UpperCAmelCase = False      ,	UpperCAmelCase = None      ,	UpperCAmelCase = True      ,	UpperCAmelCase = "arrow"      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(
					    split=UpperCAmelCase      ,	features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	keep_in_memory=UpperCAmelCase      ,	streaming=UpperCAmelCase      ,	**UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			load_from_cache_file
					lowerCAmelCase_      							=			file_format
					lowerCAmelCase_      							=			Spark(
					    df=UpperCAmelCase      ,	features=UpperCAmelCase      ,	cache_dir=UpperCAmelCase      ,	working_dir=UpperCAmelCase      ,	**UpperCAmelCase      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.streaming:
						return self.builder.as_streaming_dataset(split=self.split   )
					lowerCAmelCase_      							=			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
					self.builder.download_and_prepare(
					    download_mode=UpperCAmelCase      ,	file_format=self._file_format      ,	)
					return self.builder.as_dataset(split=self.split   )